Short-term solar radiation forecasting by advecting and diffusing MSG cloud index

Abstract

Abstract A new method for short-term solar radiation forecasting (referred to as Cloud Index Advection and Diffusion, CIADCast) is proposed and validated. The method is based on the advection and diffusion of Meteosat Second Generation (MSG) cloud index estimates using the Weather Research and Forecasting (WRF) numerical weather prediction (NWP) model. The forecasted cloud index is transformed in global horizontal irradiance (GHI) and direct normal irradiance (DNI) forecasts by means of the Heliosat-2 method. The cloud index maps are inserted in the WRF vertical layer which corresponds to the cloud height provided by a ceilometer. GHI and DNI are forecasted up to 6 h ahead with 15 min of time resolution. The method was tested using 25 days of radiometric data collected at three stations located in southern Spain. Benchmarking models such as smart persistence, a cloud motion vector (CMV) based approach and the WRF-Solar suite of the WRF model are also evaluated. Results were analyzed in the light of the different topographic characteristics of the evaluation stations areas. Results proved that CIADCast is able to provide enhanced forecasts in areas with low topographic complexity, where cloud advection by the atmospheric mesoscale dynamics is not perturbed by mountain features. In these areas, CIADCast generally outperforms the other models, especially for DNI and partially cloudy conditions. On the other hand, in regions with complex topography, where the mesoscale cloud pattern is influenced by the mountains, the performance of the CIADCast model is poor and the use of persistence or the WRF-Solar model proved to be more appropriate.